Combining video streaming and online retailing (V2R) has been a growing trend recently. In this paper, we provide practitioners and researchers in multimedia with a cloud-based platform named Hysia for easy development and deployment of V2R applications. The system consists of: 1) a back-end infrastructure providing optimized V2R related services including data engine, model repository, model serving and content matching; and 2) an application layer which enables rapid V2R application prototyping. Hysia addresses industry and academic needs in large-scale multimedia by: 1) seamlessly integrating state-of-the-art libraries including NVIDIA video SDK, Facebook faiss, and gRPC; 2) efficiently utilizing GPU computation; and 3) allowing developers to bind new models easily to meet the rapidly changing deep learning (DL) techniques. On top of that, we implement an orchestrator for further optimizing DL model serving performance. Hysia has been released as an open source project on GitHub, and attracted considerable attention. We have published Hysia to DockerHub as an official image for seamless integration and deployment in current cloud environments.

Information-centric networking (ICN) has been touted as a revolutionary solution for the future of the Internet, which will be dominated by video traffic. This work investigates the challenge of distributing video content of adaptive bitrate (ABR) over ICN. In particular, we use the in-network caching capability of ICN routers to serve users; in addition, with the help of named function, we enable ICN routers to transcode videos to lower-bitrate versions to improve the cache hit ratio. Mathematically, we formulate this design challenge into a constrained optimization problem, which aims to maximize the cache hit ratio for service providers and minimize the service delay for endusers. We design a two-step iterative algorithm to find the optimum. First, given a content management scheme, we minimize the service delay via optimally configuring the routing scheme. Second, we maximize the cache hits for a given routing policy. Finally, we rigorously prove its convergence. Through extensive simulations, we verify the convergence and the performance gains over other algorithms. We also find that more resources should be allocated to ICN routers with a heavier request rate, and the routing scheme favors the shortest path to schedule more traffic.

The emergence of smart Wi-Fi access points (AP), which are equipped with huge storage space, opens a new research area on how to utilize these resources at the edge network to improve users' quality of experience (e.g., a short startup delay and smooth playback). One important research interest in this area is content prefetching which predicts and accurately fetches contents ahead of users' requests to shift the traffic away during peak periods. However, in practice, the different video watching patterns among users and the varying network connection status lead to the time-varying server load, which eventually makes the content prefetching problem challenging. To understand this challenge, this paper first performs a large-scale measurement study on users' AP connection and TV series watching patterns using real traces. Then, based on the obtained insights, we formulate the content prefetching problem as a Markov decision process. The objective is to strike a balance between the increased prefetching and storage cost incurred by incorrect prediction and the reduced content download delay because of successful prediction. A learning-based approach is proposed to solve this problem and another three algorithms are adopted as baselines. In particular, first we investigate the performance lower bound by using a random algorithm and the upper bound by using an ideal offline approach. Then, we present a heuristic algorithm as another baseline. Finally, we design a reinforcement learning algorithm that is more practical to work in the online manner. Through extensive trace-based experiments, we demonstrate the performance gain of our design. Remarkably, our learning-based algorithm achieves a better precision and hit ratio (e.g., 80%) with about 70% (resp. 50%) cost saving compared to the random (resp. heuristic) algorithm

Cloud-centric media network (CCMN) was previously proposed to provide cost-effective content distribution services for user-generated contents (UGCs) based on media cloud. CCMN service providers orchestrate cloud resources to deliver UGCs in a pay-per-use style, with an objective to minimize the operational monetary cost. The monetary cost depends on the actual usage of cloud resources (e.g., computing, storage, and bandwidth), which in turn, is affected by the content placement strategy. In this paper, we investigate this cost-optimal content placement problem. Specifically, it is formulated into a constrained optimization problem, in which the objective is to minimize the total monetary cost, with respect to the resource capacity. We tackle this problem via a two-step strategy. The first step focuses on the placement for a single content, which is mapped into a k-center problem. Using a graph-theoretic approach, we derive and verify a logarithmic model between the optimal mean hop distance from viewers to contents, and the optimal number of content replicas. The second step leverages this analytical result to solve the cost optimization problem, via a feasible direction method. The analysis is substantiated via numerical simulations, using a set of data traces from a top content website. This investigation suggests that the optimal number of content replica for each title follows a power-law distribution in respect to its popularity rank. Moreover, it reveals a fundamental tradeoff between the storage and bandwidth cost. Finally, compared to existing heuristics, our proposed algorithm is able to obtain the optimal placement strategy, with lower computational complexity.

Given the exploding growth of video traffic, efficient video distribution is essential to the future Internet. Therefore, how to optimize its networking cost is a critical research problem. In this paper, we introduce Network Function Virtualization (NFV) in conjunction with Software-Defined Networking (SDN) to minimize the cost via joint orchestration of caching, transcoding and routing functions. Specifically, we propose a two-step iterative approach. First, in NFV-based resource allocation phase, we maximize total cache hits by optimally allocating storage and computing resources for a giving routing policy. Second, in SDN-based routing phase, we minimize the networking cost by optimally configuring the routing matrix for a given resource placement. Finally, we analytically prove their iterative repeat converges to the joint optimum. Through extensive simulations, we verify its convergence, and performance gains compared with the optimal solution of either phase alone. By examining numerical results, we obtain some operational guidelines. From the resource allocation aspect, we should allocate more resources to the node with heavier request rate. From the routing aspect, for each node-server pair, the node should split the traffic across multiple paths with identical shortest hops if there are many, or use the shortest path alone if there is only one.

Nowadays, large-scale video distribution feeds a significant fraction of the global Internet traffic. However, existing content delivery networks may not be cost efficient enough to distribute adaptive video streaming, mainly due to the lack of orchestration on storage, computing, and bandwidth resources. In this paper, we leverage Media Cloud to deliver on-demand adaptive video streaming services, where those resources can be dynamically scheduled in an on-demand fashion. Our objective is to minimize the total operational cost by optimally orchestrating multiple resources. Specifically, we formulate an optimization problem, by examining a three-way tradeoff between the caching, transcoding, and bandwidth costs, at each edge server. Then, we adopt a two-step approach to analytically derive the closed-form solution of the optimal transcoding configuration and caching space allocation, respectively, for every edge server. Finally, we verify our solution throughout extensive simulations. The results indicate that our approach achieves significant cost savings compared with the existing methods used in content delivery networks. In addition, we also find the optimal strategy and its benefits can be affected by a list of system parameters, including the unit cost of different resources, the hop distance to the origin server, the Zipf parameter of users' request patterns, and the settings of different bitrate versions for one segment.

Information centric network (ICN) has emerged as a promising architecture to efficiently distribute content over the future Internet. However, ICN proposals may still not be cost efficient enough for adaptive video streaming. The problem is, each ICN node caches duplicated copies of the same content for each bitrate version in its limited storage space. Thus the cache hit ratio drops, and the bandwidth cost of serving the cache missed requests increases. This paper proposes PAINT (Partial In-Network Transcoding) scheme to reduce the operational cost of delivering adaptive video streaming over ICN. Specifically, we consider both the in-network caching and transcoding services at each ICN node, where the storage and transcoding resources can be dynamically scheduled. Then we formulate an optimization problem to balance the trade-off between the transcoding and bandwidth costs. Next we analytically derive the optimal strategy, and quantify cost savings compared with existing schemes. Finally, we verify our solution by intensive numerical evaluations. The results indicate PAINT can achieve significant cost savings (e.g., up to 50% in typical scenarios). Besides, we find the optimal strategy and the cost savings can be affected by the cache capacity, the unit price ratio, the hop distance to origin server, and the Zipf parameter of users' request patterns.

Recently, the TV landscape is rapidly shifting from the traditional “laid-back” experience to a “lean-forward” multiscreen experience. In this paper, we propose MUTAS (MUltiscreen TV experience As a Service), a novel cloud-based service delivery model, in response to this trend. The design objective is to facilitate the development process of new multi-screen features, and improve user experiences by offering an all-in-one solution. The enabling technology is to encapsulate basic functions into a unified cloud platform, and expose divergent multi-screen services through a cloud clone per user. Based on MUTAS, we will use one system to demonstrate four different multi-screen experiences (i.e., synchronized social TV watching, video teleportation, social networking integration, and advertising re-distribution). This demo provides a reference to build cloud-based frameworks for flexible, extensible, and scalable multi-screen TV experience.

The emergence of portable devices and online social networks (OSNs) has changed the traditional video consumption paradigm by simultaneously providing multi-screen video watching, social networking engagement, etc. One challenge is to design a unified solution to support ever-growing features while guarantee system performance. In this demo, we design and implement a multi-screen technology to provide multi-screen interactions over wide area network (WAN). Furthermore, we incorporate face-detection technology into our system to identify users' bio-features and employ a machine learning based traffic scheduling mechanism to improve the system performance.

The emergence of social TV has transformed TV experiences, providing a unified media experience across different devices. In response to this trend, we have implemented a multi-screen social TV system, offering video teleportation as an attractive feature. The enabling technology is instantiating a cloud clone to support all media outlets of each user. As the user shifts his attention from one device to the other, the cloud clone might migrate to a better location to reduce its operational cost. This paper investigates this cloud clone migration problem, aiming to minimize the monetary cost on operating video teleportation. Specifically, we formulate it into a Markov Decision Problem, to balance the trade-off between the migration cost and the content transmission cost. Under this framework, four algorithms are proposed to solve this optimization problem. We first characterize an upper and a lower bound for the optimal cost, by considering a random fixed placement and an offline algorithm. We then present a semi-online and a more practical Q-learning approach to make online decisions. Their performances are evaluated based on both simulated and real user traces. The results show that the Q-learning method achieves up to 25% cost compared to random fixed placement in typical scenarios. The savings are affected by the delivery path length, the migration size, and the user behavior pattern. Moreover, our investigations reveal the optimal cloud clone location is either at the nearest or the furthest node to the user along the content delivery path for a single user scenario.

The emergence of multi-screen cloud social TV has the potential to transform TV experience, providing a unified media experience across a diverse set of devices at an affordable cost. One key technology to support unified media experience across multiple screens is to instantiate a virtual machine (VM) as a cloud clone of the user, to manage all his/her media outlets (e.g., TV and smartphone), as implemented in our Cloud-Centric Media Network (CCMN). In this case, as the user shifts his attention from one device to another, the cloud clone can migrate to another location for better quality of experience. In this paper, we investigates the problem of cloud-clone migration for the multi-screen social TV application, minimizing its monetary cost. This problem can be cast into the Markov Decision Process (MDP) framework, to balance a trade-off between the migration cost and the transmission cost. Under this framework, we first derive an upper and lower bound for the optimal monetary cost, by considering a fixed placement policy and an offline policy. We then follow up with an online policy using a dynamic programming approach. Our numerical results indicate, up to 10% monetary cost can be saved, by optimally migrating the cloud clone. Moreover, the cost reduction depends on the length of content-delivery path, the data size associated with VM migration, and the user behavior pattern. These insights would offer operational guidelines to deliver cost effective multi-screen social TV services over CCMN, potentially easing its adoption.

In recent years, technical challenges are emerging on how to efficiently distribute the rapid growing user-generated contents (UGCs) with long-tailed nature. To address this issue, we have previously proposed cloud centric media network (CCMN) for cost-efficient UGCs delivery. In this paper, we further study the content placement problem in CCMN. Our objective is to minimize the monetary cost incurred by using cloud resources to orchestrate an elastic and global content delivery network (CDN) service. In particular, this objective is achieved via a two-step method. First, for a single content, we map it into a k-center problem, and find a logarithmic relationship between the mean hop distance from users to contents, and the reciprocal of replica number. Second, for multiple contents, we formulate a convex optimization with storage and bandwidth capacity constraints, which can be solved by our proposed algorithm. Finally, we verify the algorithm based on real-world traces collected from a popular video website in China. Our numerical results suggest that, the optimal number of replica for each content follows a power law in respect to its popularity, under feasible storage and bandwidth constraints, in a set of deployed backbone networks.

Recently, there is a growing trend that people tend to consume media over multi-screens simultaneously. This paper proposes an efficient and convenient inter-screen interaction approach based on our cloud centric media network, where all the ongoing sessions on the client side are always synchronized with the media cloud. This approach realizes the session recognition and transfer over different devices in a three-step manner. First, the users are required to use the mobile device camera to scan the main screen. Second, after the screen edge detection and image correction, users are allowed to choose one or more ongoing session on the corrected image via touch screen to request the transfer. Finally, the selected sessions are identified by the cloud, and those sessions are delivered to the mobile devices to complete the session transfer. The algorithms and strategies involved are discussed in detail. We also implement a testbed on top of a private cloud at NTU. The results prove our proposed method is robust and easy to use.

Nowadays, TV experience has been transformed from the traditional "laid-back" video watching experience to a "lean-forward" social and multi-screen experience. In this demo, we design and develop a multi-screen cloud social TV system in response to this trend. Our system is built upon two enabling technologies, including a cloud based back-end infrastructure and a multi-screen front-end application. We demonstrate two key features of our system based on real user scenarios, including a living-room video watching experience with remote viewers, and the video teleportation as an enhanced multi-screen experience.

The swift adoption of cloud services is accelerating the deployment of data centers. These data centers are consuming a large amount of energy, which is expected to grow dramatically under the existing technological trends. Therefore, research efforts are in great need to architect green data centers with better energy efficiency. The most prominent approach is the consolidation enabled by virtualization. However, little effort has been paid to the potential overhead in energy usage and the throughput reduction for virtualized servers. Clear understanding of energy usage on virtualized servers lays out a solid foundation for green data-center architecture. This paper investigates how virtualization affects the energy usage in servers under different task loads, aiming to understand a fundamental trade-off between the energy saving from consolidation and the detrimental effects from virtualization. We adopt an empirical approach to measure the server energy usage with different configurations, including a benchmark case and two alternative hypervisors. Based on the collected data, we report a few findings on the impact of virtualization on server energy usage and their implications to green data-center architecture. We envision that these technical insights would bring tremendous value propositions to green data-center architecture and operations.

Leveraging cloud computing technology, we have proposed content-delivery-as-a-service (CoDaaS) to distribute user generated content (UGC) in an efficient and economical fashion. However, due to the exponential increases of Internet traffic, traditional hashing-based content routing and lookup scheme suffers from high delay. This paper introduces a global compressed counting bloom filter (CCBF) into CoDaaS to address this issue. The global CCBF adds our system with the capability to early check the existence of any specific content among all the peering surrogates, before any local checking on each cache node. Using this global CCBF, we propose two content routing and lookup mechanisms (parallel and cut-through schemes) to reduce the delay for better user experience. We verify the comparative performance of those approaches via both mathematical modeling and experimental simulation. The results show that for light traffic load, the mean response time can be saved by up to 65.2%. Besides, the impacts and overheads of different synchronization schemes for the CCBF are quantified to provide valuable insights for further optimizations.